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| # Import packages | |
| import streamlit as st | |
| import altair as alt | |
| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| from transformers import pipeline | |
| # Load pipeline | |
| pipe_lr = joblib.load(open("emotion_detector_pipe_lr.pkl","rb")) | |
| emoroberta_emotion_classifier = pipeline( | |
| "text-classification", model="arpanghoshal/EmoRoBERTa", return_all_scores=True | |
| ) | |
| # Emojis | |
| emotions_emoji_dict = {"anger":"😠","disgust":"🤮", "fear":"😨😱", "joy":"🤗", "neutral":"😐", "sadness":"😔", "shame":"😳", "surprise":"😮"} | |
| # Functions | |
| def predict_emotions(text): | |
| result = pipe_lr.predict([text]) | |
| return result[0] | |
| def get_prediction_proba(text): | |
| result = pipe_lr.predict_proba([text]) | |
| return result | |
| def predict_emotions_and_score_emoroberta(text): | |
| emotionlabel = emoroberta_emotion_classifier(text) | |
| emotion_dataframe = pd.DataFrame.from_records(emotionlabel[0]).sort_values(by=["score"], ascending=False) | |
| emotion = emotion_dataframe.iloc[0, 0] | |
| score = emotion_dataframe.iloc[0, 1] | |
| return emotion_dataframe, emotion, score | |
| def main(): | |
| st.title("Emotion Detection App") | |
| with st.form(key='emotion_detection_form'): | |
| raw_text = st.text_area("Type here") | |
| submit_text = st.form_submit_button(label = 'Submit') | |
| if submit_text: | |
| st.subheader("Logistic Regression model trained on labeled data (8 emotions)") | |
| col1,col2 = st.columns(2) | |
| prediction = predict_emotions(raw_text) | |
| prediction_probability = get_prediction_proba(raw_text) | |
| with col1: | |
| st.success("Original Text") | |
| st.write(raw_text) | |
| st.success("Emotion") | |
| emoji_icon = emotions_emoji_dict[prediction] | |
| st.write("{} {}".format(prediction,emoji_icon)) | |
| st.success("Emotion Score") | |
| st.write("{:.4f}".format(np.max(prediction_probability))) | |
| with col2: | |
| st.success("Prediction Probability") | |
| proba_df = pd.DataFrame(prediction_probability,columns = pipe_lr.classes_) | |
| proba_df_clean = proba_df.T.reset_index() | |
| proba_df_clean.columns = ["emotions","probability"] | |
| fig = alt.Chart(proba_df_clean).mark_bar().encode(y=alt.Y('emotions', sort='-x'),x='probability',color='emotions') | |
| st.altair_chart(fig,use_container_width=True) | |
| st.markdown("***") | |
| st.subheader("EmoRoBERTa - A BERT based pre-trained model fine tuned on reddit comments data(28 emotions)") | |
| col3,col4 = st.columns(2) | |
| emotion_dataframe_emoroberta, predicted_emotion_emoroberta, prediction_probability_emoroberta = predict_emotions_and_score_emoroberta(raw_text) | |
| with col3: | |
| st.success("Emotion") | |
| st.write(predicted_emotion_emoroberta) | |
| with col4: | |
| st.success("Emotion Score") | |
| st.write("{:.4f}".format(np.max(prediction_probability_emoroberta))) | |
| st.success("Prediction Probability") | |
| emotion_dataframe_emoroberta.columns = ["emotions","probability"] | |
| fig = alt.Chart(emotion_dataframe_emoroberta).mark_bar().encode(y=alt.Y('emotions', sort='-x'),x='probability',color='emotions') | |
| st.altair_chart(fig,use_container_width=True) | |
| if __name__ == '__main__': | |
| main() |